Overview

Dataset statistics

Number of variables21
Number of observations56551
Missing cells77044
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.1 MiB
Average record size in memory168.0 B

Variable types

Numeric12
Categorical5
DateTime2
Boolean1
Unsupported1

Alerts

store_and_fwd_flag is highly imbalanced (97.8%)Imbalance
improvement_surcharge is highly imbalanced (94.4%)Imbalance
payment_type is highly imbalanced (58.3%)Imbalance
trip_type is highly imbalanced (79.2%)Imbalance
congestion_surcharge is highly imbalanced (55.9%)Imbalance
store_and_fwd_flag has 3415 (6.0%) missing valuesMissing
RatecodeID has 3415 (6.0%) missing valuesMissing
passenger_count has 3415 (6.0%) missing valuesMissing
ehail_fee has 56551 (100.0%) missing valuesMissing
payment_type has 3415 (6.0%) missing valuesMissing
trip_type has 3418 (6.0%) missing valuesMissing
congestion_surcharge has 3415 (6.0%) missing valuesMissing
RatecodeID is highly skewed (γ1 = 48.08908382)Skewed
trip_distance is highly skewed (γ1 = 89.2234337)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
ehail_fee is an unsupported type, check if it needs cleaning or further analysisUnsupported
trip_distance has 2870 (5.1%) zerosZeros
extra has 32860 (58.1%) zerosZeros
mta_tax has 5169 (9.1%) zerosZeros
tip_amount has 22366 (39.6%) zerosZeros
tolls_amount has 55023 (97.3%) zerosZeros

Reproduction

Analysis started2024-04-11 04:34:22.881928
Analysis finished2024-04-11 04:35:36.468681
Duration1 minute and 13.59 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct56551
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28275
Minimum0
Maximum56550
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2024-04-11T00:35:36.981651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2827.5
Q114137.5
median28275
Q342412.5
95-th percentile53722.5
Maximum56550
Range56550
Interquartile range (IQR)28275

Descriptive statistics

Standard deviation16325.012
Coefficient of variation (CV)0.57736558
Kurtosis-1.2
Mean28275
Median Absolute Deviation (MAD)14138
Skewness0
Sum1.5989795 × 109
Variance2.6650601 × 108
MonotonicityStrictly increasing
2024-04-11T00:35:37.413589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
37705 1
 
< 0.1%
37694 1
 
< 0.1%
37695 1
 
< 0.1%
37696 1
 
< 0.1%
37697 1
 
< 0.1%
37698 1
 
< 0.1%
37699 1
 
< 0.1%
37700 1
 
< 0.1%
37701 1
 
< 0.1%
Other values (56541) 56541
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
56550 1
< 0.1%
56549 1
< 0.1%
56548 1
< 0.1%
56547 1
< 0.1%
56546 1
< 0.1%
56545 1
< 0.1%
56544 1
< 0.1%
56543 1
< 0.1%
56542 1
< 0.1%
56541 1
< 0.1%

VendorID
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size441.9 KiB
2
49213 
1
7338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters56551
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 49213
87.0%
1 7338
 
13.0%

Length

2024-04-11T00:35:37.779017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:35:38.585810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 49213
87.0%
1 7338
 
13.0%

Most occurring characters

ValueCountFrequency (%)
2 49213
87.0%
1 7338
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56551
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 49213
87.0%
1 7338
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56551
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49213
87.0%
1 7338
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49213
87.0%
1 7338
 
13.0%
Distinct55284
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size441.9 KiB
Minimum2023-12-31 14:38:47
Maximum2024-01-31 23:57:29
2024-04-11T00:35:38.963630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:39.482188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct55300
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size441.9 KiB
Minimum2023-12-31 14:46:45
Maximum2024-02-01 19:17:30
2024-04-11T00:35:39.911152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:40.270017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

store_and_fwd_flag
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing3415
Missing (%)6.0%
Memory size110.6 KiB
False
53021 
True
 
115
(Missing)
 
3415
ValueCountFrequency (%)
False 53021
93.8%
True 115
 
0.2%
(Missing) 3415
 
6.0%
2024-04-11T00:35:40.931547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

RatecodeID
Real number (ℝ)

MISSING  SKEWED 

Distinct6
Distinct (%)< 0.1%
Missing3415
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean1.151611
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2024-04-11T00:35:41.295367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum99
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.045251
Coefficient of variation (CV)0.90764243
Kurtosis4339.8336
Mean1.151611
Median Absolute Deviation (MAD)0
Skewness48.089084
Sum61192
Variance1.0925496
MonotonicityNot monotonic
2024-04-11T00:35:41.636034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 51077
90.3%
5 1867
 
3.3%
2 127
 
0.2%
4 43
 
0.1%
3 19
 
< 0.1%
99 3
 
< 0.1%
(Missing) 3415
 
6.0%
ValueCountFrequency (%)
1 51077
90.3%
2 127
 
0.2%
3 19
 
< 0.1%
4 43
 
0.1%
5 1867
 
3.3%
99 3
 
< 0.1%
ValueCountFrequency (%)
99 3
 
< 0.1%
5 1867
 
3.3%
4 43
 
0.1%
3 19
 
< 0.1%
2 127
 
0.2%
1 51077
90.3%

PULocationID
Real number (ℝ)

Distinct211
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.077594
Minimum1
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2024-04-11T00:35:42.135155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q174
median75
Q3112
95-th percentile244
Maximum265
Range264
Interquartile range (IQR)38

Descriptive statistics

Standard deviation57.862401
Coefficient of variation (CV)0.60224657
Kurtosis1.236271
Mean96.077594
Median Absolute Deviation (MAD)20
Skewness1.3369984
Sum5433284
Variance3348.0575
MonotonicityNot monotonic
2024-04-11T00:35:42.668636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 12141
21.5%
75 8458
15.0%
95 2915
 
5.2%
43 2765
 
4.9%
166 2649
 
4.7%
82 2623
 
4.6%
41 2589
 
4.6%
97 1898
 
3.4%
65 1624
 
2.9%
7 1521
 
2.7%
Other values (201) 17368
30.7%
ValueCountFrequency (%)
1 3
 
< 0.1%
3 5
 
< 0.1%
7 1521
2.7%
9 5
 
< 0.1%
10 22
 
< 0.1%
11 6
 
< 0.1%
14 25
 
< 0.1%
15 6
 
< 0.1%
16 9
 
< 0.1%
17 85
 
0.2%
ValueCountFrequency (%)
265 29
 
0.1%
264 112
 
0.2%
263 52
 
0.1%
262 3
 
< 0.1%
260 981
1.7%
259 11
 
< 0.1%
258 12
 
< 0.1%
257 2
 
< 0.1%
256 92
 
0.2%
255 190
 
0.3%

DOLocationID
Real number (ℝ)

Distinct241
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.49985
Minimum1
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2024-04-11T00:35:43.142830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q174
median140
Q3225
95-th percentile260
Maximum265
Range264
Interquartile range (IQR)151

Descriptive statistics

Standard deviation76.556276
Coefficient of variation (CV)0.54488511
Kurtosis-1.2851439
Mean140.49985
Median Absolute Deviation (MAD)66
Skewness0.09043076
Sum7945407
Variance5860.8634
MonotonicityNot monotonic
2024-04-11T00:35:43.614397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 3078
 
5.4%
74 2890
 
5.1%
236 2725
 
4.8%
238 2208
 
3.9%
41 1995
 
3.5%
166 1819
 
3.2%
42 1753
 
3.1%
263 1383
 
2.4%
95 1352
 
2.4%
239 1295
 
2.3%
Other values (231) 36053
63.8%
ValueCountFrequency (%)
1 24
 
< 0.1%
3 14
 
< 0.1%
4 58
 
0.1%
7 799
1.4%
8 6
 
< 0.1%
9 18
 
< 0.1%
10 131
 
0.2%
11 5
 
< 0.1%
12 1
 
< 0.1%
13 31
 
0.1%
ValueCountFrequency (%)
265 180
 
0.3%
264 418
 
0.7%
263 1383
2.4%
262 804
1.4%
261 39
 
0.1%
260 534
 
0.9%
259 13
 
< 0.1%
258 108
 
0.2%
257 39
 
0.1%
256 122
 
0.2%

passenger_count
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing3415
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean1.3091689
Minimum0
Maximum9
Zeros512
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2024-04-11T00:35:44.009165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.97825201
Coefficient of variation (CV)0.74723131
Kurtosis12.20348
Mean1.3091689
Median Absolute Deviation (MAD)0
Skewness3.5193124
Sum69564
Variance0.956977
MonotonicityNot monotonic
2024-04-11T00:35:44.585385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 44779
79.2%
2 4656
 
8.2%
5 1496
 
2.6%
6 902
 
1.6%
3 596
 
1.1%
0 512
 
0.9%
4 192
 
0.3%
8 2
 
< 0.1%
9 1
 
< 0.1%
(Missing) 3415
 
6.0%
ValueCountFrequency (%)
0 512
 
0.9%
1 44779
79.2%
2 4656
 
8.2%
3 596
 
1.1%
4 192
 
0.3%
5 1496
 
2.6%
6 902
 
1.6%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 2
 
< 0.1%
6 902
 
1.6%
5 1496
 
2.6%
4 192
 
0.3%
3 596
 
1.1%
2 4656
 
8.2%
1 44779
79.2%
0 512
 
0.9%

trip_distance
Real number (ℝ)

SKEWED  ZEROS 

Distinct1890
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.491124
Minimum0
Maximum201421.68
Zeros2870
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2024-04-11T00:35:45.153639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1
median1.79
Q33.08
95-th percentile7.785
Maximum201421.68
Range201421.68
Interquartile range (IQR)1.98

Descriptive statistics

Standard deviation1417.4604
Coefficient of variation (CV)45.011426
Kurtosis10445.241
Mean31.491124
Median Absolute Deviation (MAD)0.87
Skewness89.223434
Sum1780854.5
Variance2009193.9
MonotonicityNot monotonic
2024-04-11T00:35:45.752566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2870
 
5.1%
1.4 575
 
1.0%
1.3 520
 
0.9%
1.2 477
 
0.8%
1.1 454
 
0.8%
1.5 454
 
0.8%
1 406
 
0.7%
0.9 398
 
0.7%
1.6 385
 
0.7%
1.8 338
 
0.6%
Other values (1880) 49674
87.8%
ValueCountFrequency (%)
0 2870
5.1%
0.01 95
 
0.2%
0.02 83
 
0.1%
0.03 55
 
0.1%
0.04 36
 
0.1%
0.05 42
 
0.1%
0.06 48
 
0.1%
0.07 42
 
0.1%
0.08 35
 
0.1%
0.09 33
 
0.1%
ValueCountFrequency (%)
201421.68 1
< 0.1%
154650.47 1
< 0.1%
103153.6 1
< 0.1%
50508.27 1
< 0.1%
49452.14 1
< 0.1%
46383.82 1
< 0.1%
45719.16 1
< 0.1%
45369.11 1
< 0.1%
43267.14 1
< 0.1%
43134.12 1
< 0.1%

fare_amount
Real number (ℝ)

Distinct2225
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.929275
Minimum-70
Maximum1422.6
Zeros52
Zeros (%)0.1%
Negative182
Negative (%)0.3%
Memory size441.9 KiB
2024-04-11T00:35:46.343803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-70
5-th percentile5.8
Q19.3
median13.5
Q319.8
95-th percentile40
Maximum1422.6
Range1492.6
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation15.356032
Coefficient of variation (CV)0.90706964
Kurtosis1334.9067
Mean16.929275
Median Absolute Deviation (MAD)4.9
Skewness19.107181
Sum957367.44
Variance235.8077
MonotonicityNot monotonic
2024-04-11T00:35:46.831574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2795
 
4.9%
9.3 2734
 
4.8%
8.6 2703
 
4.8%
10.7 2452
 
4.3%
7.9 2427
 
4.3%
11.4 2320
 
4.1%
12.1 2228
 
3.9%
7.2 2112
 
3.7%
12.8 2067
 
3.7%
13.5 1921
 
3.4%
Other values (2215) 32792
58.0%
ValueCountFrequency (%)
-70 6
< 0.1%
-47 1
 
< 0.1%
-40.5 1
 
< 0.1%
-40 2
 
< 0.1%
-35 1
 
< 0.1%
-34 1
 
< 0.1%
-32.9 1
 
< 0.1%
-26.66 1
 
< 0.1%
-22 1
 
< 0.1%
-20 3
< 0.1%
ValueCountFrequency (%)
1422.6 1
 
< 0.1%
445.4 1
 
< 0.1%
435.6 1
 
< 0.1%
400 7
< 0.1%
309.6 1
 
< 0.1%
299 1
 
< 0.1%
277.4 1
 
< 0.1%
272.5 1
 
< 0.1%
266.2 1
 
< 0.1%
265.5 1
 
< 0.1%

extra
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90094693
Minimum-5
Maximum10.25
Zeros32860
Zeros (%)58.1%
Negative80
Negative (%)0.1%
Memory size441.9 KiB
2024-04-11T00:35:47.370865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile0
Q10
median0
Q32.5
95-th percentile2.75
Maximum10.25
Range15.25
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.3443133
Coefficient of variation (CV)1.4921115
Kurtosis3.7822981
Mean0.90094693
Median Absolute Deviation (MAD)0
Skewness1.744451
Sum50949.45
Variance1.8071782
MonotonicityNot monotonic
2024-04-11T00:35:47.694762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 32860
58.1%
2.5 11386
 
20.1%
1 9243
 
16.3%
2.75 991
 
1.8%
5 763
 
1.3%
5.25 600
 
1.1%
7.5 341
 
0.6%
3.75 198
 
0.4%
6 57
 
0.1%
-1 39
 
0.1%
Other values (7) 73
 
0.1%
ValueCountFrequency (%)
-5 2
 
< 0.1%
-2.5 39
 
0.1%
-1 39
 
0.1%
0 32860
58.1%
0.5 20
 
< 0.1%
0.7 1
 
< 0.1%
1 9243
 
16.3%
2.5 11386
 
20.1%
2.75 991
 
1.8%
3.25 9
 
< 0.1%
ValueCountFrequency (%)
10.25 1
 
< 0.1%
7.5 341
 
0.6%
6 57
 
0.1%
5.5 1
 
< 0.1%
5.25 600
 
1.1%
5 763
 
1.3%
3.75 198
 
0.4%
3.25 9
 
< 0.1%
2.75 991
 
1.8%
2.5 11386
20.1%

mta_tax
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57669626
Minimum-0.5
Maximum4.25
Zeros5169
Zeros (%)9.1%
Negative162
Negative (%)0.3%
Memory size441.9 KiB
2024-04-11T00:35:48.152123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile0
Q10.5
median0.5
Q30.5
95-th percentile1.5
Maximum4.25
Range4.75
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3819979
Coefficient of variation (CV)0.66239012
Kurtosis2.67179
Mean0.57669626
Median Absolute Deviation (MAD)0
Skewness1.4558102
Sum32612.75
Variance0.1459224
MonotonicityNot monotonic
2024-04-11T00:35:48.773368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.5 44140
78.1%
1.5 7055
 
12.5%
0 5169
 
9.1%
-0.5 162
 
0.3%
1 20
 
< 0.1%
4.25 5
 
< 0.1%
ValueCountFrequency (%)
-0.5 162
 
0.3%
0 5169
 
9.1%
0.5 44140
78.1%
1 20
 
< 0.1%
1.5 7055
 
12.5%
4.25 5
 
< 0.1%
ValueCountFrequency (%)
4.25 5
 
< 0.1%
1.5 7055
 
12.5%
1 20
 
< 0.1%
0.5 44140
78.1%
0 5169
 
9.1%
-0.5 162
 
0.3%

tip_amount
Real number (ℝ)

ZEROS 

Distinct1384
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2565101
Minimum-1.66
Maximum110
Zeros22366
Zeros (%)39.6%
Negative9
Negative (%)< 0.1%
Memory size441.9 KiB
2024-04-11T00:35:49.419377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.66
5-th percentile0
Q10
median2
Q33.5
95-th percentile6.92
Maximum110
Range111.66
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.8479567
Coefficient of variation (CV)1.2621068
Kurtosis83.537732
Mean2.2565101
Median Absolute Deviation (MAD)2
Skewness4.6230429
Sum127607.9
Variance8.1108572
MonotonicityNot monotonic
2024-04-11T00:35:49.916037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22366
39.6%
2 2221
 
3.9%
1 1722
 
3.0%
3 1171
 
2.1%
5 646
 
1.1%
4 459
 
0.8%
2.3 434
 
0.8%
2.16 379
 
0.7%
1.5 372
 
0.7%
2.5 357
 
0.6%
Other values (1374) 26424
46.7%
ValueCountFrequency (%)
-1.66 1
 
< 0.1%
-1.46 1
 
< 0.1%
-0.9 1
 
< 0.1%
-0.8 1
 
< 0.1%
-0.01 5
 
< 0.1%
0 22366
39.6%
0.01 117
 
0.2%
0.02 40
 
0.1%
0.03 16
 
< 0.1%
0.04 5
 
< 0.1%
ValueCountFrequency (%)
110 1
 
< 0.1%
88 1
 
< 0.1%
70.5 1
 
< 0.1%
70 1
 
< 0.1%
56 1
 
< 0.1%
53 1
 
< 0.1%
50 3
< 0.1%
47 1
 
< 0.1%
45.61 1
 
< 0.1%
44 1
 
< 0.1%

tolls_amount
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19120175
Minimum0
Maximum24.05
Zeros55023
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2024-04-11T00:35:50.420171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum24.05
Range24.05
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1907482
Coefficient of variation (CV)6.2277055
Kurtosis57.45046
Mean0.19120175
Median Absolute Deviation (MAD)0
Skewness6.9219463
Sum10812.65
Variance1.4178812
MonotonicityNot monotonic
2024-04-11T00:35:50.889560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 55023
97.3%
6.94 1378
 
2.4%
3.18 68
 
0.1%
5.2 18
 
< 0.1%
13.88 18
 
< 0.1%
13.38 10
 
< 0.1%
14.75 9
 
< 0.1%
15.38 5
 
< 0.1%
20.82 4
 
< 0.1%
20.32 4
 
< 0.1%
Other values (8) 14
 
< 0.1%
ValueCountFrequency (%)
0 55023
97.3%
2.75 2
 
< 0.1%
3.18 68
 
0.1%
5.2 18
 
< 0.1%
6.55 1
 
< 0.1%
6.94 1378
 
2.4%
10.12 1
 
< 0.1%
12.14 2
 
< 0.1%
12.75 4
 
< 0.1%
13.38 10
 
< 0.1%
ValueCountFrequency (%)
24.05 1
 
< 0.1%
22.32 2
 
< 0.1%
20.82 4
 
< 0.1%
20.32 4
 
< 0.1%
15.5 1
 
< 0.1%
15.38 5
 
< 0.1%
14.75 9
< 0.1%
13.88 18
< 0.1%
13.38 10
< 0.1%
12.75 4
 
< 0.1%

ehail_fee
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing56551
Missing (%)100.0%
Memory size441.9 KiB

improvement_surcharge
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size441.9 KiB
1.0
55743 
0.3
 
447
0.0
 
179
-1.0
 
179
-0.3
 
3

Length

Max length4
Median length3
Mean length3.0032183
Min length3

Characters and Unicode

Total characters169835
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 55743
98.6%
0.3 447
 
0.8%
0.0 179
 
0.3%
-1.0 179
 
0.3%
-0.3 3
 
< 0.1%

Length

2024-04-11T00:35:51.318817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:35:51.625432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 55922
98.9%
0.3 450
 
0.8%
0.0 179
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 56730
33.4%
. 56551
33.3%
1 55922
32.9%
3 450
 
0.3%
- 182
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113102
66.6%
Other Punctuation 56551
33.3%
Dash Punctuation 182
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56730
50.2%
1 55922
49.4%
3 450
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 56551
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56730
33.4%
. 56551
33.3%
1 55922
32.9%
3 450
 
0.3%
- 182
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 56730
33.4%
. 56551
33.3%
1 55922
32.9%
3 450
 
0.3%
- 182
 
0.1%

total_amount
Real number (ℝ)

Distinct4530
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.403186
Minimum-76.5
Maximum1424.1
Zeros41
Zeros (%)0.1%
Negative185
Negative (%)0.3%
Memory size441.9 KiB
2024-04-11T00:35:51.864836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-76.5
5-th percentile8.7
Q113.44
median18.42
Q326.6
95-th percentile49.25
Maximum1424.1
Range1500.6
Interquartile range (IQR)13.16

Descriptive statistics

Standard deviation16.956518
Coefficient of variation (CV)0.75687973
Kurtosis888.36618
Mean22.403186
Median Absolute Deviation (MAD)5.94
Skewness14.485147
Sum1266922.6
Variance287.52349
MonotonicityNot monotonic
2024-04-11T00:35:52.177662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 620
 
1.1%
10.8 601
 
1.1%
15 538
 
1.0%
10.1 522
 
0.9%
12.9 517
 
0.9%
11.5 493
 
0.9%
8.7 472
 
0.8%
9.4 460
 
0.8%
12.2 450
 
0.8%
13.8 433
 
0.8%
Other values (4520) 51445
91.0%
ValueCountFrequency (%)
-76.5 1
 
< 0.1%
-71.5 4
< 0.1%
-71 1
 
< 0.1%
-48 1
 
< 0.1%
-46 1
 
< 0.1%
-41.5 1
 
< 0.1%
-41 1
 
< 0.1%
-36 1
 
< 0.1%
-35 1
 
< 0.1%
-31.9 1
 
< 0.1%
ValueCountFrequency (%)
1424.1 1
 
< 0.1%
446.9 1
 
< 0.1%
437.1 1
 
< 0.1%
401.5 1
 
< 0.1%
401 6
< 0.1%
311.1 1
 
< 0.1%
300 1
 
< 0.1%
278.9 1
 
< 0.1%
276.5 1
 
< 0.1%
271.05 1
 
< 0.1%

payment_type
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3415
Missing (%)6.0%
Memory size441.9 KiB
1.0
36660 
2.0
15913 
3.0
 
434
4.0
 
128
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159408
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 36660
64.8%
2.0 15913
28.1%
3.0 434
 
0.8%
4.0 128
 
0.2%
5.0 1
 
< 0.1%
(Missing) 3415
 
6.0%

Length

2024-04-11T00:35:52.450872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:35:52.727870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 36660
69.0%
2.0 15913
29.9%
3.0 434
 
0.8%
4.0 128
 
0.2%
5.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 53136
33.3%
0 53136
33.3%
1 36660
23.0%
2 15913
 
10.0%
3 434
 
0.3%
4 128
 
0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106272
66.7%
Other Punctuation 53136
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53136
50.0%
1 36660
34.5%
2 15913
 
15.0%
3 434
 
0.4%
4 128
 
0.1%
5 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 53136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159408
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 53136
33.3%
0 53136
33.3%
1 36660
23.0%
2 15913
 
10.0%
3 434
 
0.3%
4 128
 
0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 53136
33.3%
0 53136
33.3%
1 36660
23.0%
2 15913
 
10.0%
3 434
 
0.3%
4 128
 
0.1%
5 1
 
< 0.1%

trip_type
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing3418
Missing (%)6.0%
Memory size441.9 KiB
1.0
51397 
2.0
 
1736

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159399
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 51397
90.9%
2.0 1736
 
3.1%
(Missing) 3418
 
6.0%

Length

2024-04-11T00:35:52.938799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:35:53.152370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 51397
96.7%
2.0 1736
 
3.3%

Most occurring characters

ValueCountFrequency (%)
. 53133
33.3%
0 53133
33.3%
1 51397
32.2%
2 1736
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106266
66.7%
Other Punctuation 53133
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53133
50.0%
1 51397
48.4%
2 1736
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 53133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159399
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 53133
33.3%
0 53133
33.3%
1 51397
32.2%
2 1736
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159399
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 53133
33.3%
0 53133
33.3%
1 51397
32.2%
2 1736
 
1.1%

congestion_surcharge
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing3415
Missing (%)6.0%
Memory size441.9 KiB
0.0
38099 
2.75
14890 
2.5
 
143
-2.75
 
4

Length

Max length5
Median length3
Mean length3.2803749
Min length3

Characters and Unicode

Total characters174306
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.75
2nd row2.75
3rd row2.75
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 38099
67.4%
2.75 14890
 
26.3%
2.5 143
 
0.3%
-2.75 4
 
< 0.1%
(Missing) 3415
 
6.0%

Length

2024-04-11T00:35:53.343827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T00:35:53.677819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 38099
71.7%
2.75 14894
 
28.0%
2.5 143
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 76198
43.7%
. 53136
30.5%
2 15037
 
8.6%
5 15037
 
8.6%
7 14894
 
8.5%
- 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121166
69.5%
Other Punctuation 53136
30.5%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76198
62.9%
2 15037
 
12.4%
5 15037
 
12.4%
7 14894
 
12.3%
Other Punctuation
ValueCountFrequency (%)
. 53136
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174306
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76198
43.7%
. 53136
30.5%
2 15037
 
8.6%
5 15037
 
8.6%
7 14894
 
8.5%
- 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76198
43.7%
. 53136
30.5%
2 15037
 
8.6%
5 15037
 
8.6%
7 14894
 
8.5%
- 4
 
< 0.1%

Interactions

2024-04-11T00:35:27.990111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:33.599543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:39.551494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:45.018162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:50.477068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:55.408163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:01.355043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:04.986331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:09.122938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:12.610548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:17.900688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:23.022429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:28.398618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:34.253689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:39.968071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:45.430313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:50.912302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:55.902509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:02.145160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:05.220675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:09.719665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:13.097409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:18.334003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:23.370616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:28.785466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:34.553040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:40.482133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:45.823961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:51.256990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:56.352030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:02.534872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:05.456866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:09.939595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:13.569176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:18.801284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:23.778131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:29.283343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:34.778328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:41.019891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:46.332011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:51.651557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:56.887197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:02.857029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:05.669464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:10.175109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:13.945928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:19.200699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:24.217121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:29.733633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:35.269067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:41.399821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:46.668190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:52.190446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:57.231016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:03.102093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:05.912256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:10.395132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:14.417447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:19.586128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:24.604729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:30.234392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:35.579083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:41.667698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:47.035113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:52.603400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:57.635074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:03.309593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:06.118939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:10.599369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:14.748102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:20.034504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:24.974704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:30.842963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:36.178126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:42.212631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:47.519625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:52.852250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:58.556482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:03.584682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:06.384084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:10.850802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:15.093653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:20.401263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:25.528117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:31.186903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:36.686361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:42.767794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:47.969111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:53.085484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:59.103465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:03.819801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:06.605289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:11.196391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:15.596674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:20.850466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:25.891924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:31.715642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:37.552880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:43.096378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:48.470636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:53.526150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:59.593263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:04.073640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:06.884609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:11.431801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:16.162344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:21.276427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:26.267493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:32.173139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:37.991792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:43.465191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:48.842971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:53.959022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:59.803536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:04.285394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:07.384668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:11.651893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:16.551139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:21.700494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:26.829418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:32.715671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:38.516143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:43.792917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:49.456187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:54.478167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:00.031965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:04.523831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:08.061368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:11.872026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:16.902695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:22.126689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:27.266034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:33.269167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:38.980199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:44.382092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:49.991818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:34:54.949196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:00.676085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:04.736026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:08.716002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:12.186111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:17.386396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:22.520510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T00:35:27.621505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-04-11T00:35:33.621675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-11T00:35:34.802848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-11T00:35:35.809257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0VendorIDlpep_pickup_datetimelpep_dropoff_datetimestore_and_fwd_flagRatecodeIDPULocationIDDOLocationIDpassenger_counttrip_distancefare_amountextramta_taxtip_amounttolls_amountehail_feeimprovement_surchargetotal_amountpayment_typetrip_typecongestion_surcharge
0022024-01-01 00:46:552024-01-01 00:58:25N1.02362391.01.9812.81.000.53.610.0NaN1.021.661.01.02.75
1122024-01-01 00:31:422024-01-01 00:52:34N1.0651705.06.5430.31.000.57.110.0NaN1.042.661.01.02.75
2222024-01-01 00:30:212024-01-01 00:49:23N1.0742621.03.0819.81.000.53.000.0NaN1.028.051.01.02.75
3312024-01-01 00:30:202024-01-01 00:42:12N1.0741161.02.4014.21.001.50.000.0NaN1.016.702.01.00.00
4422024-01-01 00:32:382024-01-01 00:43:37N1.0742431.05.1422.61.000.56.280.0NaN1.031.381.01.00.00
5512024-01-01 00:43:412024-01-01 01:00:23N1.0332091.02.0017.03.751.52.000.0NaN1.024.251.01.02.75
6612024-01-01 00:31:562024-01-01 00:48:09N1.0742382.03.2018.43.751.54.700.0NaN1.028.351.01.02.75
7722024-01-01 00:46:122024-01-01 00:57:39N1.01662392.02.0113.51.000.55.620.0NaN1.024.371.01.02.75
8822024-01-01 00:38:072024-01-01 00:39:23N1.02262261.00.313.71.000.50.000.0NaN1.06.202.01.00.00
9922024-01-01 00:44:242024-01-01 00:57:47N1.071291.02.3214.91.000.53.480.0NaN1.020.881.01.00.00
Unnamed: 0VendorIDlpep_pickup_datetimelpep_dropoff_datetimestore_and_fwd_flagRatecodeIDPULocationIDDOLocationIDpassenger_counttrip_distancefare_amountextramta_taxtip_amounttolls_amountehail_feeimprovement_surchargetotal_amountpayment_typetrip_typecongestion_surcharge
565415654122024-01-31 18:32:522024-01-31 18:43:13NaNNaN7542NaN1.9315.000.00.00.000.0NaN1.016.00NaNNaNNaN
565425654222024-01-31 18:19:002024-01-31 18:35:00NaNNaN18861NaN1.6012.610.00.02.720.0NaN1.016.33NaNNaNNaN
565435654322024-01-31 19:23:002024-01-31 19:31:00NaNNaN166151NaN1.0911.740.00.02.000.0NaN1.014.74NaNNaNNaN
565445654422024-01-31 19:14:002024-01-31 19:23:00NaNNaN193146NaN1.5211.380.00.01.240.0NaN1.013.62NaNNaNNaN
565455654522024-01-31 19:41:002024-01-31 19:57:00NaNNaN41237NaN2.7516.390.00.01.010.0NaN1.021.15NaNNaNNaN
565465654622024-01-31 20:46:002024-01-31 20:55:00NaNNaN3325NaN0.0011.580.00.03.140.0NaN1.015.72NaNNaNNaN
565475654722024-01-31 21:06:002024-01-31 21:11:00NaNNaN7272NaN0.4911.580.00.00.000.0NaN1.012.58NaNNaNNaN
565485654822024-01-31 21:36:002024-01-31 21:40:00NaNNaN7272NaN0.5211.580.00.02.520.0NaN1.015.10NaNNaNNaN
565495654922024-01-31 22:45:002024-01-31 22:51:00NaNNaN4142NaN1.1714.220.00.00.000.0NaN1.015.22NaNNaNNaN
565505655022024-01-31 22:28:002024-01-31 22:59:00NaNNaN3391NaN9.2744.620.00.04.560.0NaN1.050.18NaNNaNNaN